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Diabetes Technol Ther. 2018 Nov 7. doi: 10.1089/dia.2018.0247. [Epub ahead of print]

Resistance to Data Loss of Glycemic Variability Measurements in Long-Term Continuous Glucose Monitoring.

Author information

1
1 Department of Biostatistics and Translational Medicine, Medical University of Lodz , Lodz, Poland .
2
2 Institute of Applied Computer Science, Lodz University of Technology , Lodz, Poland .
3
3 Department of Pediatrics, Oncology, Haematology and Diabetology, Medical University of Lodz , Lodz, Poland .
4
4 Department of Radiation Oncology, Dana-Farber Cancer Institute , Boston, Massachusetts.

Abstract

BACKGROUND:

Continuous glucose monitoring (CGM) is a method of estimating blood glucose values from those recorded in the interstitial fluid. Because increasingly longer CGM measurements are possible, errors and data loss become more and more likely and potentially more damaging to accurate calculations of glycemic variability (GV) indices. Our research investigates the resistance of the CGM recording to data loss.

METHODS:

We collected 71 CGM recordings (duration of min: 2, max: 265, median: 42 days) from patients with type 1 diabetes and used three algorithms to introduce missing data. We calculated mean and standard deviation (SD) of absolute percentage error of 12 variability indices and correlated those with the percentage of missing data and duration of the measurements.

RESULTS:

Mean absolute percentage error of variability indices increased linearly with the percentage of missing data along with SD of absolute percentage error. Except for mean amplitude of glycemic excursions and time spent in hypoglycemia, all absolute errors never exceeded 25%, while mean absolute errors stayed below 5%. The gradient removal algorithm introduced errors larger than the single datapoint and block removal algorithms. The absolute percentage error of variability indices correlated negatively with the duration of the CGM measurements.

CONCLUSIONS:

Standard GV measurements in long-term glucose monitoring are robustly resistant to data loss.

KEYWORDS:

Blood glucose; Computer-assisted signal processing; Continuous glucose monitoring; Data loss.; Diabetes mellitus; Glycemic variability

PMID:
30403500
DOI:
10.1089/dia.2018.0247

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